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Get Started Free →A flat-file, agent-friendly mirror of every NY Tech Week 2026 event (June 1–7, NYC). Use when planning a Tech Week schedule, filtering events for a specific person/company, or analyzing the event landscape (top hosts, neighborhoods, capacity). 1,410 events with full descriptions, RSVP links, hero images, and 2,406 resolved host profiles.
.claude/skills/stage-11-agentics-ny-tech-week-2026/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 195% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 228% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 100% | 0% |
You are working inside a community-maintained mirror of tech-week.com/calendar/nyc, restructured as flat markdown files so other agents can read, filter, and rank without scraping the live calendar.
ny-tech-week-reference/
├── README.md ← human-readable overview
├── SKILL.md ← you are here
├── INDEX.md ← chronological + by-host + by-neighborhood listing
├── HOST-STATS.md ← top 50 hosts + recurring co-host pairs
├── manifest.json ← raw calendar extraction
├── users.json ← 2,406 resolved Partiful profiles (id → name, bio, photo, socials, tags)
├── events/ ← 1,410 markdown files, one per event
└── images/ ← 1,401 hero images (PNG/JPG), keyed by event_idEach events/*.md has YAML frontmatter and a body. Filename is YYYY-MM-DD-HHMM-<slug>.md — sortable by start time.
| Field | Type | Notes | |-------|------|-------| | title | string | From Partiful | | event_id | string | Partiful event ID (last segment of RSVP URL) | | date, day | string | ISO date + day name in ET | | start_time, end_time | string | "10:00am ET" — pretty form | | start_iso, end_iso | ISO 8601 | UTC, for date math | | host | string | Calendar's joined host string (up to 2 names visible) | | owner_count | int | True host count from Partiful (includes platform admin — see Quirks) | | owner_ids | liststring] | All Partiful user IDs; join with users.json for names + bios | | venue_name, venue_address | string | From Partiful's structured location | | neighborhood | string | NYC neighborhood like Chelsea, Midtown, Virtual (NYC) | | rsvp_url, public_short_url | URL | Partiful links | | google_maps | URL | Pre-formed maps query | | is_capped, max_capacity, remaining_capacity | bool / int | When the host capped the guestlist | | going_guest_count, total_guest_count, approved_guest_count | int | Guest count at crawl time (2026-05-30) | | at_capacity | bool | Whether RSVPs are currently closed | | canceled | bool | true if the host marked the event canceled on Partiful | | canceled_at | ISO 8601 | When cancellation was logged (only when canceled: true) | | canceled_by | string | Partiful user ID that triggered the cancellation | | cancellation_message | string | The host's note explaining why (verbatim, may be multi-line) | | guest_action | "APPLY" or "RSVP" | APPLY = hosts approve; RSVP = open | | visibility | string | "public" for the entire dataset | | badges | liststring] | ["Sponsored"], ["Morning"], etc. | | image | URL | Partiful Firebase image URL (may expire) | | local_image | string | Path to archived image in images/ (use this, not image) | | fetch_status | "ok" or "failed" | 7 events are stubs (Partiful 404'd at crawl time) |
# {title}

**Hosts:** {calendar host string} _(+N more on Partiful)_
**When:** {pretty date}
**Where:** {venue · address · neighborhood}
**Capacity:** capped at N · X taken, Y remaining
**Going:** N of M
**Access:** apply / hosts approve
**RSVP:** {url}
**Map:** {google maps url}
### Hosts on Partiful
- [{name}](https://partiful.com/u/{id}) — _{bio}_
- [{name}](https://partiful.com/u/{id})
- _(plus Partiful platform admin auto-added to most events)_
## Description
{full Partiful description, verbatim}
---
[Apply on Partiful →]({url})7DFu4rITofNzKIjA7hCx appears in owner_ids for ~99% of events (1,362 of 1,374). Treat it as noise — it's an automated TechWeek/Partiful account, not a real host. Filter at analysis time:python real_hosts = [oid for oid in owner_ids if oid != "7DFu4rITofNzKIjA7hCx"] owner_count includes it to match Partiful's UI count.
is_capped: false, max_capacity and remaining_capacity will be absent. Use going_guest_count / total_guest_count for uncapped events.fetch_status: failed) had their Partiful pages 404 at crawl time. Frontmatter is sparse, body is a stub with the RSVP URL. They still exist in events/ for completeness — handle by checking fetch_status first.host: "Foo, Bar" shows up to 2 hosts. For the true list, look at ### Hosts on Partiful in the body, or join owner_ids against users.json.Pitch and Rum) is dated 2026-06-11, outside the official June 1–7 window. It's on tech-week.com's calendar so it stays.CANCELED on Partiful, we keep the file (so the URL stays resolvable, the data stays diff-able across crawls, and downstream agents can see what was cancelled and why) but flag it loudly: canceled: true in frontmatter, a ⚠ CANCELED banner at the top of the body with the host's cancellation_message. Filter at analysis time:python live_events = [f for f in events if not f.get("canceled")]
CHANGES-2026-05-30.md for the diff). Event details, RSVP availability, and guest counts may have drifted since.bashgrep -l 'date: 2026-06-02' events/*.md \ | xargs grep -l 'neighborhood: "SoHo"'
bashgrep -l -i 'agent' events/*.md \ | xargs grep -l -iE '(orchestrat|autonomous|multi-?agent|MCP)'
See HOST-STATS.md, or compute from events/*.md owner_ids joined with users.json:
pythonimport json, re, pathlib, collections users = json.load(open("users.json")) counts = collections.Counter() for f in pathlib.Path("events").glob("*.md"): m = re.search(r'owner_ids:\s*(\[[^\]]+\])', f.read_text()) if m: for uid in json.loads(m.group(1)): if uid != "7DFu4rITofNzKIjA7hCx": counts[uid] += 1 for uid, n in counts.most_common(10): print(n, users.get(uid, {}).get("name"))
Score each event against the person's interests (use their bio, role, or stated themes as keyword seeds), then pick top N per day. See scripts/score.py for the pattern used to score events for Atin Woodard / Stage 11. Do not modify individual event files for a person-specific shortlist — write the picks to a new file like picks-for-<name>.md. Keep events/ neutral so it stays useful to everyone.
bashgrep -l '^canceled: true' events/*.md | while read f; do title=$(grep -m1 '^title:' "$f" | sed 's/title: //; s/^"//; s/"$//') msg=$(grep -m1 '^cancellation_message:' "$f" || true) printf "%s\n %s\n %s\n\n" "$title" "$f" "$msg" done
Where is_capped: true and remaining_capacity is small relative to max_capacity:
pythonimport re, json, pathlib for f in pathlib.Path("events").glob("*.md"): s = f.read_text() if re.search(r"is_capped:\s*true", s): max_c = int(re.search(r"max_capacity:\s*(\d+)", s).group(1)) rem = int(re.search(r"remaining_capacity:\s*(\d+)", s).group(1)) if rem / max_c < 0.15: title = re.search(r'title:\s*"([^"]+)"', s).group(1) print(f"{rem}/{max_c} remaining: {title}")
NYTW data shifts daily. To refresh:
c11 / Playwright to script the scroll).scripts/fetch.py against the regenerated manifest.scripts/enrich.py to refresh capacity + image data.scripts/resolve_hosts.py to re-resolve any new host IDs.scripts/render_body.py to regenerate the rendered markdown.scripts/build_index.py and scripts/host_stats.py.Each step is idempotent — re-runnable without duplication.
rsvp_url so humans can verify. tech-week.com is the source of truth; this repo is a convenience layer.events/ is neutral. Person-specific picks belong in separate files.Event metadata © respective hosts and tech-week.com / a16z. Partiful images © Partiful + respective uploaders. This dataset is shared under fair-use for community planning and agent tooling.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 6,308 | 3,048 | -52% | 1 | 1 | 0% | 1,106 | 3,260 | +195% | 0 | 0 | — |
case-02 | fail→fail | 7,739 | 18,297 | +136% | 1 | 1 | 0% | 1,257 | 3,875 | +208% | 0 | 0 | — |
case-03 | fail→fail | 22,049 | 12,604 | -43% | 1 | 1 | 0% | 4,074 | 2,993 | -27% | 0 | 0 | — |
case-04 | pass→pass | 12,341 | 15,283 | +24% | 1 | 1 | 0% | 2,588 | 5,805 | +124% | 0 | 0 | — |
case-05 | pass→pass | 15,554 | 19,186 | +23% | 1 | 1 | 0% | 3,585 | 7,117 | +99% | 0 | 0 | — |
case-06 | pass→pass | 9,042 | 10,433 | +15% | 1 | 1 | 0% | 1,837 | 4,827 | +163% | 0 | 0 | — |
case-07 | pass→pass | 16,412 | 10,543 | -36% | 1 | 1 | 0% | 3,226 | 5,356 | +66% | 0 | 0 | — |
case-08 | fail→pass | 15,768 | 4,945 | -69% | 1 | 1 | 0% | 3,110 | 3,610 | +16% | 0 | 0 | — |
case-09 | pass→pass | 8,480 | 4,231 | -50% | 1 | 1 | 0% | 1,577 | 3,399 | +116% | 0 | 0 | — |
case-10 | fail→pass | 6,571 | 6,540 | -0% | 1 | 1 | 0% | 1,251 | 4,106 | +228% | 0 | 0 | — |
case-11 | fail→pass | 13,466 | 1,756 | -87% | 1 | 1 | 0% | 2,662 | 3,033 | +14% | 0 | 0 | — |
case-12 | fail→pass | 9,485 | 6,038 | -36% | 1 | 1 | 0% | 1,989 | 3,987 | +100% | 0 | 0 | — |
case-13 | fail→pass | 7,110 | 7,217 | +2% | 1 | 1 | 0% | 1,457 | 4,193 | +188% | 0 | 0 | — |
case-14 | fail→fail | 8,640 | 4,596 | -47% | 1 | 1 | 0% | 1,796 | 3,575 | +99% | 0 | 0 | — |
case-15 | fail→pass | 11,664 | 3,142 | -73% | 1 | 1 | 0% | 2,158 | 3,365 | +56% | 0 | 0 | — |
case-16 | fail→pass | 7,061 | 3,982 | -44% | 1 | 1 | 0% | 1,366 | 3,501 | +156% | 0 | 0 | — |
case-17 | pass→pass | 9,980 | 7,762 | -22% | 1 | 1 | 0% | 2,149 | 4,440 | +107% | 0 | 0 | — |
case-18 | fail→pass | 13,982 | 12,313 | -12% | 1 | 1 | 0% | 3,105 | 4,919 | +58% | 0 | 0 | — |
case-19 | pass→pass | 11,464 | 7,698 | -33% | 1 | 1 | 0% | 2,483 | 4,393 | +77% | 0 | 0 | — |
case-20 | fail→pass | 7,467 | 3,545 | -53% | 1 | 1 | 0% | 1,447 | 3,369 | +133% | 0 | 0 | — |
case-21 | fail→pass | 11,998 | 7,262 | -39% | 1 | 1 | 0% | 2,692 | 4,318 | +60% | 0 | 0 | — |
case-22 | fail→pass | 10,606 | 3,721 | -65% | 1 | 1 | 0% | 2,082 | 3,495 | +68% | 0 | 0 | — |
case-23 | fail→pass | 8,851 | 1,698 | -81% | 1 | 1 | 0% | 1,792 | 3,022 | +69% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +57 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.